{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "  \"script\": \"train.py\",\n",
      "  \"arguments\": [\n",
      "\n",
      "  ],\n",
      "  \"target\": \"cpu\",\n",
      "  \"framework\": \"Python\",\n",
      "  \"communicator\": \"None\",\n",
      "  \"autoPrepareEnvironment\": true,\n",
      "  \"maxRunDurationSeconds\": null,\n",
      "  \"nodeCount\": 1,\n",
      "  \"environment\": {\n",
      "    \"name\": null,\n",
      "    \"version\": null,\n",
      "    \"environmentVariables\": {\n",
      "      \"EXAMPLE_ENV_VAR\": \"EXAMPLE_VALUE\"\n",
      "    },\n",
      "    \"python\": {\n",
      "      \"userManagedDependencies\": false,\n",
      "      \"interpreterPath\": \"python\",\n",
      "      \"condaDependenciesFile\": \".azureml/conda_dependencies.yml\",\n",
      "      \"baseCondaEnvironment\": null\n",
      "    },\n",
      "    \"docker\": {\n",
      "      \"enabled\": true,\n",
      "      \"baseImage\": \"mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04\",\n",
      "      \"sharedVolumes\": true,\n",
      "      \"gpuSupport\": false,\n",
      "      \"shmSize\": \"1g\",\n",
      "      \"arguments\": [\n",
      "\n",
      "      ],\n",
      "      \"baseImageRegistry\": {\n",
      "        \"address\": null,\n",
      "        \"username\": null,\n",
      "        \"password\": null\n",
      "      }\n",
      "    }\n",
      "  },\n",
      "  \"history\": {\n",
      "    \"outputCollection\": true,\n",
      "    \"snapshotProject\": true,\n",
      "    \"directoriesToWatch\": [\n",
      "      \"logs\"\n",
      "    ]\n",
      "  },\n",
      "  \"mpi\": {\n",
      "    \"processCountPerNode\": 1\n",
      "  },\n",
      "  \"dataReferences\": {\n",
      "  },\n",
      "  \"sourceDirectoryDataStore\": null,\n",
      "  \"amlcompute\": {\n",
      "    \"vmSize\": \"STANDARD_D2_V2\",\n",
      "    \"vmPriority\": null,\n",
      "    \"retainCluster\": false,\n",
      "    \"name\": null,\n",
      "    \"clusterMaxNodeCount\": 1\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "# see what your run definition looks like\n",
    "with open(\".azureml/explain.runconfig\",\"r\") as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Conda environment specification. The dependencies defined in this file will\n",
      "# be automatically provisioned for runs with userManagedDependencies=False.\n",
      "\n",
      "# Details about the Conda environment file format:\n",
      "# https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually\n",
      "\n",
      "name: project_environment\n",
      "dependencies:\n",
      "  # The python interpreter version.\n",
      "  # Currently Azure ML only supports 3.5.2 and later.\n",
      "- python=3.6.2\n",
      "\n",
      "- pip:\n",
      "  - --index-url https://azuremlsdktestpypi.azureedge.net/AzureML-Contrib-Explain-Model-Gated/3010237\n",
      "  - --extra-index-url https://pypi.python.org/simple\n",
      "  - sklearn_pandas\n",
      "  - azureml-defaults<0.1.50\n",
      "  - azureml-contrib-explain-model<0.1.50\n",
      "  - azureml-core<0.1.50\n",
      "  - azureml-telemetry<0.1.50\n",
      "  - azureml-explain-model<0.1.50\n",
      "- scikit-learn\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# see what your package dependencies looks like\n",
    "with open(\".azureml/conda_dependencies.yml\",\"r\") as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING - Warning: Falling back to use azure cli login credentials.\n",
      "If you run your code in unattended mode, i.e., where you can't give a user input, then we recommend to use ServicePrincipalAuthentication or MsiAuthentication.\n",
      "Please refer to aka.ms/aml-notebook-auth for different authentication mechanisms in azureml-sdk.\n"
     ]
    },
    {
     "ename": "ExperimentExecutionException",
     "evalue": "{\n    \"error_details\": {\n        \"correlation\": {\n            \"operation\": \"519553e8b4a79c4e89b387dfcb5635d1\",\n            \"request\": \"XF1oBHL5g1A=\"\n        },\n        \"environment\": \"eastus2\",\n        \"error\": {\n            \"code\": \"UserError\",\n            \"debugInfo\": {\n                \"message\": \"Unknown compute target cpu\",\n                \"stackTrace\": \"   at Microsoft.MachineLearning.Execution.Services.StrategyState.get_ComputeTargetType() in /home/vsts/work/1/s/src/azureml-api/src/Execution/Services/StrategyState.cs:line 186\\n   at Microsoft.MachineLearning.Execution.Services.ExecutionStrategyFactory.Create(StrategyState state) in /home/vsts/work/1/s/src/azureml-api/src/Execution/Services/ExecutionStrategy.cs:line 89\\n   at Microsoft.MachineLearning.Execution.EntryPoints.Api.Controllers.ExecutionController.StartRun(ConfigurationManager configurationManager, Guid subscriptionId, String resourceGroupName, String workspaceName, String experimentName, RunDefinition definition, Stream zipStream, RunId runId) in /home/vsts/work/1/s/src/azureml-api/src/Execution/EntryPoints/Api/Controllers/ExecutionController.cs:line 621\\n   at Microsoft.MachineLearning.Execution.EntryPoints.Api.Controllers.ExecutionController.StartRun(Guid subscriptionId, String resourceGroupName, String workspaceName, String experimentName, ICollection`1 files, RunId runId) in /home/vsts/work/1/s/src/azureml-api/src/Execution/EntryPoints/Api/Controllers/ExecutionController.cs:line 271\\n   at lambda_method(Closure , Object )\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.InvokeActionMethodAsync()\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.InvokeNextActionFilterAsync()\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.Rethrow(ActionExecutedContext context)\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.Next(State& next, Scope& scope, Object& state, Boolean& isCompleted)\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.InvokeInnerFilterAsync()\\n   at Microsoft.AspNetCore.Mvc.Internal.ResourceInvoker.InvokeNextExceptionFilterAsync()\",\n                \"type\": \"Microsoft.MachineLearning.Common.WebApi.Exceptions.BadRequestException\"\n            },\n            \"message\": \"Unknown compute target cpu\"\n        },\n        \"location\": \"eastus2\",\n        \"time\": \"2019-06-12T12:39:52.5139778+00:00\"\n    },\n    \"status_code\": 400,\n    \"url\": \"https://eastus2.experiments.azureml.net/execution/v1.0/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourceGroups/shipatelrg/providers/Microsoft.MachineLearningServices/workspaces/shipatelws/experiments/generate-attrition-explainer/run?runId=generate-attrition-explainer_1560343182_4197953b\"\n}",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mExperimentExecutionException\u001b[0m              Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-2-fad4338d3217>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[0mrunconfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mRunConfiguration\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'explain'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[0msrc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mScriptRunConfig\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msource_directory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'.'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mscript\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'train_explain.py'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mrun_config\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrunconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m \u001b[0mrun\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mExperiment\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mws\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'generate-attrition-explainer'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msubmit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[0mRunDetails\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\envs\\myenv\\lib\\site-packages\\azureml\\core\\experiment.py\u001b[0m in \u001b[0;36msubmit\u001b[1;34m(self, config, tags, **kwargs)\u001b[0m\n\u001b[0;32m    143\u001b[0m         \u001b[0msubmit_func\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_experiment_submit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    144\u001b[0m         \u001b[1;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_log_context\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"submit config {}\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 145\u001b[1;33m             \u001b[0mrun\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msubmit_func\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mconfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mworkspace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    146\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mtags\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    147\u001b[0m             \u001b[0mrun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_tags\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtags\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\envs\\myenv\\lib\\site-packages\\azureml\\core\\script_run_config.py\u001b[0m in \u001b[0;36msubmit\u001b[1;34m(script_run_config, workspace, experiment_name)\u001b[0m\n\u001b[0;32m     33\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     34\u001b[0m     run = _commands.start_run(project, run_config,\n\u001b[1;32m---> 35\u001b[1;33m                               telemetry_values=script_run_config._telemetry_values)\n\u001b[0m\u001b[0;32m     36\u001b[0m     \u001b[0mrun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_properties\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mglobal_tracking_info_registry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgather_all\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscript_run_config\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msource_directory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\envs\\myenv\\lib\\site-packages\\azureml\\_execution\\_commands.py\u001b[0m in \u001b[0;36mstart_run\u001b[1;34m(project_object, run_config_object, run_id, injected_files, telemetry_values)\u001b[0m\n\u001b[0;32m    113\u001b[0m                                \u001b[0mcustom_target_dict\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcustom_target_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrun_id\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrun_id\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    114\u001b[0m                                \u001b[0minjected_files\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minjected_files\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 115\u001b[1;33m                                telemetry_values=telemetry_values)\n\u001b[0m\u001b[0;32m    116\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    117\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\envs\\myenv\\lib\\site-packages\\azureml\\_execution\\_commands.py\u001b[0m in \u001b[0;36m_start_internal\u001b[1;34m(project_object, run_config_object, prepare_only, prepare_check, custom_target_dict, run_id, injected_files, telemetry_values)\u001b[0m\n\u001b[0;32m    408\u001b[0m             \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mrequests\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpost\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0muri\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdefinition\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    409\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 410\u001b[1;33m         \u001b[0m_raise_request_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"starting run\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    411\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    412\u001b[0m     \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Continuum\\miniconda3\\envs\\myenv\\lib\\site-packages\\azureml\\_execution\\_commands.py\u001b[0m in \u001b[0;36m_raise_request_error\u001b[1;34m(response, action)\u001b[0m\n\u001b[0;32m    548\u001b[0m         \u001b[1;31m# response.text is a JSON from execution service.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    549\u001b[0m         \u001b[0mresponse_message\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_http_exception_response_string\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 550\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mExperimentExecutionException\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresponse_message\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    551\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mExperimentExecutionException\u001b[0m: {\n    \"error_details\": {\n        \"correlation\": {\n            \"operation\": \"519553e8b4a79c4e89b387dfcb5635d1\",\n            \"request\": \"XF1oBHL5g1A=\"\n        },\n        \"environment\": \"eastus2\",\n        \"error\": {\n            \"code\": \"UserError\",\n            \"debugInfo\": {\n                \"message\": \"Unknown compute target cpu\",\n                \"stackTrace\": \"   at Microsoft.MachineLearning.Execution.Services.StrategyState.get_ComputeTargetType() in /home/vsts/work/1/s/src/azureml-api/src/Execution/Services/StrategyState.cs:line 186\\n   at Microsoft.MachineLearning.Execution.Services.ExecutionStrategyFactory.Create(StrategyState state) in /home/vsts/work/1/s/src/azureml-api/src/Execution/Services/ExecutionStrategy.cs:line 89\\n   at Microsoft.MachineLearning.Execution.EntryPoints.Api.Controllers.ExecutionController.StartRun(ConfigurationManager configurationManager, Guid subscriptionId, String resourceGroupName, String workspaceName, String experimentName, RunDefinition definition, Stream zipStream, RunId runId) in /home/vsts/work/1/s/src/azureml-api/src/Execution/EntryPoints/Api/Controllers/ExecutionController.cs:line 621\\n   at Microsoft.MachineLearning.Execution.EntryPoints.Api.Controllers.ExecutionController.StartRun(Guid subscriptionId, String resourceGroupName, String workspaceName, String experimentName, ICollection`1 files, RunId runId) in /home/vsts/work/1/s/src/azureml-api/src/Execution/EntryPoints/Api/Controllers/ExecutionController.cs:line 271\\n   at lambda_method(Closure , Object )\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.InvokeActionMethodAsync()\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.InvokeNextActionFilterAsync()\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.Rethrow(ActionExecutedContext context)\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.Next(State& next, Scope& scope, Object& state, Boolean& isCompleted)\\n   at Microsoft.AspNetCore.Mvc.Internal.ControllerActionInvoker.InvokeInnerFilterAsync()\\n   at Microsoft.AspNetCore.Mvc.Internal.ResourceInvoker.InvokeNextExceptionFilterAsync()\",\n                \"type\": \"Microsoft.MachineLearning.Common.WebApi.Exceptions.BadRequestException\"\n            },\n            \"message\": \"Unknown compute target cpu\"\n        },\n        \"location\": \"eastus2\",\n        \"time\": \"2019-06-12T12:39:52.5139778+00:00\"\n    },\n    \"status_code\": 400,\n    \"url\": \"https://eastus2.experiments.azureml.net/execution/v1.0/subscriptions/92c76a2f-0e1c-4216-b65e-abf7a3f34c1e/resourceGroups/shipatelrg/providers/Microsoft.MachineLearningServices/workspaces/shipatelws/experiments/generate-attrition-explainer/run?runId=generate-attrition-explainer_1560343182_4197953b\"\n}"
     ]
    }
   ],
   "source": [
    "#submit and monitor a run\n",
    "#pip install --upgrade azureml-sdk[explain,automl]\n",
    "\n",
    "from azureml.core import RunConfiguration, Experiment, Run\n",
    "from azureml.core.script_run_config import ScriptRunConfig\n",
    "from azureml.widgets import RunDetails\n",
    "from azureml.core import Workspace\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "runconfig = RunConfiguration.load('.','explain')\n",
    "src = ScriptRunConfig(source_directory='.',script='train_explain.py',run_config=runconfig)\n",
    "run = Experiment(ws,'generate-attrition-explainer').submit(src)\n",
    "RunDetails(run).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Please supply at least one subcommand: disable, enable, install, list, uninstall\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "^C\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "'source' is not recognized as an internal or external command,\n",
      "operable program or batch file.\n"
     ]
    }
   ],
   "source": [
    "!jupyter nbextension install --py --user azureml.contrib.explain.model.visualize\n",
    "!jupyter nbextension enable --py --user azureml.contrib.explain.model.visualize\n",
    "!source activate py36 && jupyter nbextension list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'numpy.core._multiarray_umath'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'numpy.core._multiarray_umath'"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'azureml.contrib.explain.model.visualize'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-ebc09fc056aa>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mazureml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvisualize\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mExplanationDashboard\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mazureml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplain\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplanation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexplanation_client\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mExplanationClient\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mazureml\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmodel\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mModel\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexternals\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mjoblib\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'azureml.contrib.explain.model.visualize'"
     ]
    }
   ],
   "source": [
    "from azureml.contrib.explain.model.visualize import ExplanationDashboard\n",
    "from azureml.contrib.explain.model.explanation.explanation_client import ExplanationClient\n",
    "from azureml.core.model import Model\n",
    "from sklearn.externals import joblib\n",
    "\n",
    "original_model = Model(ws, 'IBM_attrition_model')\n",
    "model_path = original_model.download(exist_ok=True)\n",
    "\n",
    "client = ExplanationClient.from_run(run)\n",
    "global_explanation = client.download_model_explanation()\n",
    "attrition_model = joblib.load(model_path)\n",
    "\n",
    "x_test_path = './x_test.pkl'\n",
    "run.download_file('x_test_ibm.pkl', output_file_path=x_test_path)\n",
    "\n",
    "x_test = joblib.load(x_test_path)\n",
    "\n",
    "ExplanationDashboard(global_explanation, attrition_model, x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#see what your score file looks like\n",
    "with open(\"score.py\",\"r\") as f:\n",
    "    print(f.read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#deploy\n",
    "from azureml.core import Workspace\n",
    "from azureml.core.webservice import AciWebservice\n",
    "from azureml.core.model import InferenceConfig, Model\n",
    "\n",
    "ws = Workspace.from_config()\n",
    "\n",
    "scoring_explainer_model = Model(ws, 'IBM_attrition_explainer')\n",
    "attrition_model = Model(ws, 'IBM_attrition_model')\n",
    "\n",
    "aciconfig = AciWebservice.deploy_configuration(cpu_cores=1, \n",
    "                                               memory_gb=1, \n",
    "                                               tags={\"data\": \"IBM_Attrition\",  \n",
    "                                                     \"method\" : \"local_explanation\"}, \n",
    "                                               description='Explain predictions on employee attrition')\n",
    "                                               \n",
    "inference_config = InferenceConfig(entry_script='score.py', \n",
    "                                   extra_docker_file_steps='Dockerfile', \n",
    "                                   runtime='python', \n",
    "                                   conda_file='myenv.yml')\n",
    "\n",
    "service = Model.deploy(ws, name='predictattritionsvc', models=[scoring_explainer_model, attrition_model], inference_config= inference_config, deployment_config=aciconfig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "http://52.191.239.245:80/score\n",
      "prediction: {\"predictions\": [0], \"local_importance_values\": [[[0.011893157926831918, 0.0, 0.001948017948270056, 0.0, 0.007483265451196458, 0.0, 0.009660853271403366, -0.017192954971353896, 0.0, 0.0, -0.010145356542845527, -0.016782511147139297, -0.0015557671219139056, 0.0031978832881977143, 0.006866099843478735, 0.02188772568434794, 0.0004925454707126985, 0.0023098392350914057, 0.0, 0.000373374398902053, 0.0, 0.0015539969560965233, 0.0026309279472540546, 0.02159462439982303, -0.0052022765712637885, 0.012417684525554779, 0.012723466385322294, -0.012818438311330942, 0.008945499626888669, -0.014813315343381466]], [[-0.011893157926831911, 0.0, -0.0019480179482700873, 0.0, -0.007483265451196458, 0.0, -0.009660853271403376, 0.01719295497135389, 0.0, 0.0, 0.010145356542845525, 0.016782511147139287, 0.001555767121913916, -0.0031978832881977212, -0.006866099843478756, -0.021887725684347947, -0.0004925454707127019, -0.0023098392350914404, 0.0, -0.000373374398902053, 0.0, -0.0015539969560965233, -0.002630927947254065, -0.02159462439982302, 0.005202276571263792, -0.01241768452555475, -0.01272346638532229, 0.012818438311330947, -0.008945499626888676, 0.014813315343381457]]]}\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get URI for querying\n",
    "from azureml.core.webservice import AciWebservice\n",
    "import requests\n",
    "import json\n",
    "\n",
    "sample = '{\"Age\":{\"899\":49},\"BusinessTravel\":{\"899\":\"Travel_Rarely\"},\"DailyRate\":{\"899\":1098},\"Department\":{\"899\":\"Research & Development\"},\"DistanceFromHome\":{\"899\":4},\"Education\":{\"899\":2},\"EducationField\":{\"899\":\"Medical\"},\"EnvironmentSatisfaction\":{\"899\":1},\"Gender\":{\"899\":\"Male\"},\"HourlyRate\":{\"899\":85},\"JobInvolvement\":{\"899\":2},\"JobLevel\":{\"899\":5},\"JobRole\":{\"899\":\"Manager\"},\"JobSatisfaction\":{\"899\":3},\"MaritalStatus\":{\"899\":\"Married\"},\"MonthlyIncome\":{\"899\":18711},\"MonthlyRate\":{\"899\":12124},\"NumCompaniesWorked\":{\"899\":2},\"OverTime\":{\"899\":\"No\"},\"PercentSalaryHike\":{\"899\":13},\"PerformanceRating\":{\"899\":3},\"RelationshipSatisfaction\":{\"899\":3},\"StockOptionLevel\":{\"899\":1},\"TotalWorkingYears\":{\"899\":23},\"TrainingTimesLastYear\":{\"899\":2},\"WorkLifeBalance\":{\"899\":4},\"YearsAtCompany\":{\"899\":1},\"YearsInCurrentRole\":{\"899\":0},\"YearsSinceLastPromotion\":{\"899\":0},\"YearsWithCurrManager\":{\"899\":0}}'\n",
    "\n",
    "service = AciWebservice(ws,\"predictattritionsvc\")\n",
    "print(service.scoring_uri)\n",
    "\n",
    "headers = {'Content-Type':'application/json'}\n",
    "\n",
    "# send request to service\n",
    "resp = requests.post(service.scoring_uri, sample, headers=headers)\n",
    "print(\"prediction:\", resp.text)\n",
    "result = json.loads(resp.text)\n",
    "\n",
    "#plot the feature importance for the prediction\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt; plt.rcdefaults()\n",
    "import json\n",
    "\n",
    "labels = json.loads(sample)\n",
    "labels = labels.keys()\n",
    "objects = labels\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = result[\"local_importance_values\"][0][0]\n",
    "\n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "locs, labels = plt.xticks()\n",
    "plt.setp(labels, rotation=90)\n",
    "plt.ylabel('Feature impact - leaving vs not leaving')\n",
    "plt.title('Local feature importance for prediction')\n",
    "\n",
    "plt.show()"
   ]
  }
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